{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Requirement already satisfied: wordcloud in c:\\programdata\\anaconda3\\lib\\site-packages (1.9.4)\n",
      "Requirement already satisfied: numpy>=1.6.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from wordcloud) (1.15.1)\n",
      "Requirement already satisfied: pillow in c:\\programdata\\anaconda3\\lib\\site-packages (from wordcloud) (9.5.0)\n",
      "Requirement already satisfied: matplotlib in c:\\programdata\\anaconda3\\lib\\site-packages (from wordcloud) (2.2.3)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib->wordcloud) (0.10.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib->wordcloud) (2.2.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib->wordcloud) (2.7.3)\n",
      "Requirement already satisfied: pytz in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib->wordcloud) (2018.5)\n",
      "Requirement already satisfied: six>=1.10 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib->wordcloud) (1.11.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from matplotlib->wordcloud) (1.0.1)\n",
      "Requirement already satisfied: setuptools in c:\\programdata\\anaconda3\\lib\\site-packages (from kiwisolver>=1.0.1->matplotlib->wordcloud) (40.2.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEPRECATION: pandas 0.23.4 has a non-standard dependency specifier pytz>=2011k. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pandas or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\n"
     ]
    }
   ],
   "source": [
    "!pip install -i https://pypi.tuna.tsinghua.edu.cn/simple wordcloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Requirement already satisfied: imageio in c:\\programdata\\anaconda3\\lib\\site-packages (2.4.1)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEPRECATION: pandas 0.23.4 has a non-standard dependency specifier pytz>=2011k. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pandas or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\n"
     ]
    }
   ],
   "source": [
    "!pip install -i https://pypi.tuna.tsinghua.edu.cn/simple imageio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Requirement already satisfied: pillow in c:\\programdata\\anaconda3\\lib\\site-packages (9.5.0)\n",
      "Requirement already satisfied: pip in c:\\programdata\\anaconda3\\lib\\site-packages (24.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEPRECATION: pandas 0.23.4 has a non-standard dependency specifier pytz>=2011k. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pandas or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\n"
     ]
    }
   ],
   "source": [
    "!pip install pillow --upgrade pip -i https://pypi.tuna.tsinghua.edu.cn/simple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple\n",
      "Collecting gensim\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/8a/6f/a690547cb7089d4019465bfbfbbb8bea5b3e52969cd2d6005049e6678ec4/gensim-4.2.0-cp37-cp37m-win_amd64.whl (24.0 MB)\n",
      "     ---------------------------------------- 24.0/24.0 MB 3.0 MB/s eta 0:00:00\n",
      "Collecting numpy>=1.17.0 (from gensim)\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/97/9f/da37cc4a188a1d5d203d65ab28d6504e17594b5342e0c1dc5610ee6f4535/numpy-1.21.6-cp37-cp37m-win_amd64.whl (14.0 MB)\n",
      "     ---------------------------------------- 14.0/14.0 MB 3.9 MB/s eta 0:00:00\n",
      "Requirement already satisfied: scipy>=0.18.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from gensim) (1.1.0)\n",
      "Collecting smart-open>=1.8.1 (from gensim)\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/7a/18/9a8d9f01957aa1f8bbc5676d54c2e33102d247e146c1a3679d3bd5cc2e3a/smart_open-7.1.0-py3-none-any.whl (61 kB)\n",
      "     ---------------------------------------- 61.7/61.7 kB 1.1 MB/s eta 0:00:00\n",
      "Collecting Cython==0.29.28 (from gensim)\n",
      "  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/9f/79/311cfbca90332ab37ef8ea08f1af3266f20a9a0e7a1d652842db832226bb/Cython-0.29.28-py2.py3-none-any.whl (983 kB)\n",
      "     -------------------------------------- 983.8/983.8 kB 3.5 MB/s eta 0:00:00\n",
      "Requirement already satisfied: wrapt in c:\\programdata\\anaconda3\\lib\\site-packages (from smart-open>=1.8.1->gensim) (1.10.11)\n",
      "Installing collected packages: smart-open, numpy, Cython, gensim\n",
      "  Attempting uninstall: numpy\n",
      "    Found existing installation: numpy 1.15.1\n",
      "    Uninstalling numpy-1.15.1:\n",
      "      Successfully uninstalled numpy-1.15.1\n",
      "  Attempting uninstall: Cython\n",
      "    Found existing installation: Cython 0.28.5\n",
      "    Uninstalling Cython-0.28.5:\n",
      "      Successfully uninstalled Cython-0.28.5\n",
      "Successfully installed Cython-0.29.28 gensim-4.2.0 numpy-1.21.6 smart-open-7.1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DEPRECATION: pandas 0.23.4 has a non-standard dependency specifier pytz>=2011k. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pandas or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\n",
      "  WARNING: Failed to remove contents in a temporary directory 'c:\\programdata\\anaconda3\\lib\\site-packages\\~umpy'.\n",
      "  You can safely remove it manually.\n"
     ]
    }
   ],
   "source": [
    "!pip install -i https://pypi.tuna.tsinghua.edu.cn/simple gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "将'张三'替换为'杜晓豪'后的结果： 杜晓豪个人网页的网址为http://abc138qaz，喜欢语文、数学和科学\n",
      "['数学', '科学']\n",
      "张三个人网页的网址为http://abc***qaz，喜欢语文、数学和科学。\n",
      "['http://abc']\n"
     ]
    }
   ],
   "source": [
    "#task1\n",
    "import re\n",
    "text='张三个人网页的网址为http://abc138qaz，喜欢语文、数学和科学。'\n",
    "p_string=text.split('。')\n",
    "for line in p_string:\n",
    "    if re.search('张三',line) is not None:\n",
    "        line=re.sub('张三','杜晓豪',line)\n",
    "        print(\"将'张三'替换为'杜晓豪'后的结果：\",line)\n",
    "print(re.findall('[数,科]+学',text))\n",
    "print(re.sub('\\d','*',text))\n",
    "print(re.findall('h.+c',text))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "对文本进行关键词提取：\n",
      " ['盐差', '潮汐能', '运输', '海流', '温差']\n"
     ]
    }
   ],
   "source": [
    "#task2\n",
    "import re\n",
    "import jieba.analyse\n",
    "import jieba.posseg\n",
    "sample=open(r'./sea.txt',encoding='utf-8').read()\n",
    "def get_keywords(text):\n",
    "    keywords=jieba.analyse.extract_tags(text,topK=100,withWeight=False,allowPOS=('n','vn','v'))\n",
    "    return keywords\n",
    "keywords=get_keywords(sample)\n",
    "print(\"对文本进行关键词提取：\\n\",keywords[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#task2\n",
    "import PIL\n",
    "import jieba\n",
    "from wordcloud import WordCloud\n",
    "import matplotlib.pyplot as plt\n",
    "import imageio\n",
    "#读取中文文本\n",
    "f=open(r'./sea.txt',encoding='utf-8')\n",
    "t=f.read()\n",
    "f.close()\n",
    "words=jieba.lcut(t)\n",
    "txt=' '.join(words)\n",
    "image=imageio.imread(r'./horse.png')\n",
    "w=WordCloud(font_path=r'./FZFSJW.TTF',background_color='white',mask=image)\n",
    "w.generate(txt)\n",
    "w.to_file(r'./test.png')\n",
    "plt.imshow(w,interpolation='bilinear')\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "与'文化'相似度最高的10个词：\n",
      "[('地理', 0.6726233959197998), ('娱乐', 0.6667584180831909), ('交流', 0.6620886921882629), ('艺术', 0.6593754887580872), ('现代', 0.6548150777816772), ('戏剧', 0.640352189540863), ('素养', 0.636569619178772), ('地域', 0.6347382664680481), ('哲学', 0.6332722902297974), ('文学', 0.626044511795044)]\n"
     ]
    }
   ],
   "source": [
    "import numpy\n",
    "import gensim\n",
    "import numpy as np\n",
    "from jieba import analyse\n",
    "from gensim.models import Word2Vec\n",
    "from gensim.models.word2vec import LineSentence\n",
    "\n",
    "def train_word2vec():     #训练Word2Vec模型\n",
    "    #读取已分词的数据集\n",
    "    cor_data=open('./TrainData.txt','r',encoding='utf-8')\n",
    "    model=Word2Vec(LineSentence(cor_data),sg=0,vector_size=200,window=5,min_count=5,workers=9)   #训练模型\n",
    "    model.save('./model_word2vec')   #保存模型\n",
    "    print(\"与'文化'相似度最高的10个词：\")\n",
    "    print(model.wv.most_similar('文化',topn=10))\n",
    "train_word2vec()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def keyword(data):   #提取关键词\n",
    "    tfidf=analyse.extract_tags\n",
    "    #使用TF-IDF算法提取关键词\n",
    "    keywords=tfidf(data)\n",
    "    return keywords\n",
    "def get_keywords(docpath,savepath):   #提取新闻文本关键词\n",
    "    with open(docpath, 'r', encoding='utf-8') as docf,open(savepath, 'w') as outf:\n",
    "        for data in docf:    #读取每一行数据\n",
    "            data = data[:len(data)-1]   #删除行尾的换行符\n",
    "            keywords=keyword(data)      #提取关键词\n",
    "            for word in keywords:\n",
    "                outf.write(word + ' ')\n",
    "            outf.write('\\n')\n",
    "def get_pos(string,char):\n",
    "    space_pos=[]   #初始化一个空列表，用于存储字符在字符串中的位置\n",
    "    try:\n",
    "        #获取字符串的索引和值，如果值为查找的字符，添加到列表\n",
    "        space_pos = list(((pos)for pos, val in enumerate(string) if (val == char)))\n",
    "    except:\n",
    "        pass\n",
    "    return space_pos\n",
    "#用训练好的模型和关键词计算文本向量\n",
    "def get_vector(file_name, model):\n",
    "    with open(file_name, 'r') as f:\n",
    "        wordvec_size = 200  #定义词向量维度 \n",
    "        word_vector = numpy.zeros(wordvec_size)   #生成全零的数组\n",
    "        for data in f:\n",
    "            space_pos = get_pos(data,' ')   #获取空格的位置\n",
    "            first_word=data[0:space_pos[0]]  #提取第一个词\n",
    "            #判断模型是否包含第一个词\n",
    "            if model.wv.__contains__(first_word):\n",
    "                word_vector=word_vector + model.wv[first_word]\n",
    "            #除第一个词外的其他词\n",
    "            for i in range(len(space_pos) - 1):\n",
    "                word = data[space_pos[i]: space_pos[i+1]]\n",
    "                if model.wv.__contains__(word):\n",
    "                    word_vector = word_vector + model.wv[word]\n",
    "    return word_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#计算两个文本向量的相似度\n",
    "def similarity(vector1,vector2):\n",
    "    vector1_abs=np.sqrt(vector1.dot(vector1))\n",
    "    vector2_abs=np.sqrt(vector2.dot(vector2))\n",
    "    if vector2_abs != 0 and vector1_abs !=0:\n",
    "        similarity = (vector1.dot(vector2))/(vector1_abs * vector2_abs)\n",
    "    else:\n",
    "        similarity=0\n",
    "    return similarity\n",
    "def main():\n",
    "    model=gensim.models.Word2Vec.load('./model_word2vec')\n",
    "    zuoye1 = './zuoye1.txt'\n",
    "    zuoye2 = './zuoye2.txt'\n",
    "    new1_keywords='./new1_keywords.txt'\n",
    "    new2_keywords='./new2_keywords.txt'\n",
    "    get_keywords(zuoye1,new1_keywords)\n",
    "    get_keywords(zuoye2,new2_keywords)\n",
    "    new1_vector=get_vector(new1_keywords, model)\n",
    "    print('文本zuoye1的部分向量：\\n',new1_vector[:20])\n",
    "    new2_vector=get_vector(new2_keywords, model)\n",
    "    print('文本zuoye2的部分向量：\\n',new2_vector[:20])\n",
    "    print('文本zuoye1和文本zuoye2的相似度：',similarity(new1_vector,new2_vector))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "文本zuoye1的部分向量：\n",
      " [ 1.08867098 -0.74772432 -2.68698919  0.29410177 -0.70420574  0.92900521\n",
      "  1.01303256  1.37274593 -0.09091707 -0.80824133 -1.5586065   1.18298626\n",
      "  1.94222176  0.35552776 -2.04653102  0.76162514 -0.88477966 -1.46282712\n",
      " -0.22760118 -1.19733399]\n",
      "文本zuoye2的部分向量：\n",
      " [-0.64477523 -0.35234125 -1.08223092 -1.10071185  0.1284799   0.8360177\n",
      "  0.47022105  1.61786976  0.13831291 -0.5911258  -0.14875607  0.79702249\n",
      "  1.42249515  2.32507849 -1.01220497  0.00918084 -1.2144624  -0.04736412\n",
      " -0.04867887 -1.23845515]\n",
      "文本zuoye1和文本zuoye2的相似度： 0.5107637012494259\n"
     ]
    }
   ],
   "source": [
    "if __name__ == '__main__':\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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